Hettmansperger / McKean Robust Nonparametric Statistical Methods, Second Edition

2. Auflage 2011
ISBN: 978-1-4398-0909-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 554 Seiten

Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models. It follows the approach of the first edition by developing rank-based methods from the unifying theme of geometry. This edition, however, includes more models and methods and significantly extends the possible analyses based on ranks.
New to the Second Edition

- A new section on rank procedures for nonlinear models

- A new chapter on models with dependent error structure, covering rank methods for mixed models, general estimating equations, and time series

- New material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models

Taking a comprehensive, unified approach to statistical analysis, the book continues to describe one- and two-sample problems, the basic development of rank methods in the linear model, and fixed effects experimental designs. It also explores models with dependent error structure and multivariate models. The authors illustrate the implementation of the methods using many real-world examples and R. More information about the data sets and R packages can be found at www.crcpress.com

Zielgruppe

Researchers and graduate students in statistics.

Autoren/Hrsg.

Hettmansperger, Thomas P.

McKean, Joseph W.

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

One-Sample Problems
Introduction
Location Model
Geometry and Inference in the Location Model
Examples
Properties of Norm-Based Inference
Robustness Properties of Norm-Based Inference
Inference and the Wilcoxon Signed-Rank Norm
Inference Based on General Signed-Rank Norms
Ranked Set Sampling
L1 Interpolated Confidence Intervals
Two-Sample Analysis
Two-Sample Problems
Introduction
Geometric Motivation
Examples
Inference Based on the Mann-Whitney-Wilcoxon
General Rank Scores
L1 Analyses
Robustness Properties
Proportional Hazards
Two-Sample Rank Set Sampling (RSS)
Two-Sample Scale Problem
Behrens-Fisher Problem
Paired Designs
Linear Models
Introduction
Geometry of Estimation and Tests
Examples
Assumptions for Asymptotic Theory
Theory of Rank-Based Estimates
Theory of Rank-Based Tests
Implementation of the R Analysis
L1 Analysis
Diagnostics
Survival Analysis
Correlation Model
High Breakdown (HBR) Estimates
Diagnostics for Differentiating between Fits
Rank-Based Procedures for Nonlinear Models
Experimental Designs: Fixed Effects
Introduction
One-Way Design
Multiple Comparison Procedures
Two-Way Crossed Factorial
Analysis of Covariance
Further Examples
Rank Transform
Models with Dependent Error Structure
Introduction
General Mixed Models
Simple Mixed Models
Arnold Transformations
General Estimating Equations (GEE)
Time Series
Multivariate
Multivariate Location Model
Componentwise
Spatial Methods
Affine Equivariant and Invariant Methods
Robustness of Estimates of Location
Linear Model
Experimental Designs
Appendix: Asymptotic Results
References
Index

Über Autor(innen)

Thomas P. Hettmansperger is a professor emeritus of statistics at Penn State University. Dr. Hettmansperger is a fellow of the American Statistical Association and Institute of Mathematical Statistics and an elected member of the International Statistical Institute. His research interests span nonparametric statistics, robust methods, and mixture models.
Joseph W. McKean is a professor of statistics at Western Michigan University. His research interests include robust nonparametric procedures for linear, nonlinear, and mixed models and times series designs. A fellow of the American Statistical Association, Dr. McKean has developed highly efficient and high breakdown procedures.